Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations180268
Missing cells11
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.4 MiB
Average record size in memory136.0 B

Variable types

Numeric10
Categorical5
DateTime2

Reproduction

Analysis started2025-03-27 23:07:31.493598
Analysis finished2025-03-27 23:07:44.963084
Duration13.47 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

ID_Equipo
Real number (ℝ)

Distinct500
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean247.98524
Minimum1
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-28T00:07:45.045575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile27
Q1123
median245
Q3373
95-th percentile474
Maximum500
Range499
Interquartile range (IQR)250

Descriptive statistics

Standard deviation144.01723
Coefficient of variation (CV)0.5807492
Kurtosis-1.2115979
Mean247.98524
Median Absolute Deviation (MAD)125
Skewness0.026268111
Sum44703803
Variance20740.962
MonotonicityIncreasing
2025-03-28T00:07:45.127881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
236 792
 
0.4%
493 780
 
0.4%
49 702
 
0.4%
98 690
 
0.4%
470 675
 
0.4%
15 650
 
0.4%
37 650
 
0.4%
331 638
 
0.4%
338 625
 
0.3%
90 621
 
0.3%
Other values (490) 173445
96.2%
ValueCountFrequency (%)
1 330
0.2%
2 528
0.3%
3 228
0.1%
4 441
0.2%
5 252
0.1%
6 456
0.3%
7 260
0.1%
8 240
0.1%
9 240
0.1%
10 182
 
0.1%
ValueCountFrequency (%)
500 1
 
< 0.1%
499 225
 
0.1%
498 525
0.3%
497 230
 
0.1%
496 288
 
0.2%
495 320
0.2%
494 240
 
0.1%
493 780
0.4%
492 320
0.2%
491 230
 
0.1%

Tipo_Equipo
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Transformador
46916 
Motor
46290 
Compresor
44554 
Generador
42508 

Length

Max length13
Median length9
Mean length9.0138904
Min length5

Characters and Unicode

Total characters1624916
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompresor
2nd rowCompresor
3rd rowCompresor
4th rowCompresor
5th rowCompresor

Common Values

ValueCountFrequency (%)
Transformador 46916
26.0%
Motor 46290
25.7%
Compresor 44554
24.7%
Generador 42508
23.6%

Length

2025-03-28T00:07:45.239605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T00:07:45.308179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
transformador 46916
26.0%
motor 46290
25.7%
compresor 44554
24.7%
generador 42508
23.6%

Most occurring characters

ValueCountFrequency (%)
r 361162
22.2%
o 318028
19.6%
a 136340
 
8.4%
e 129570
 
8.0%
s 91470
 
5.6%
m 91470
 
5.6%
n 89424
 
5.5%
d 89424
 
5.5%
T 46916
 
2.9%
f 46916
 
2.9%
Other values (5) 224196
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1624916
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 361162
22.2%
o 318028
19.6%
a 136340
 
8.4%
e 129570
 
8.0%
s 91470
 
5.6%
m 91470
 
5.6%
n 89424
 
5.5%
d 89424
 
5.5%
T 46916
 
2.9%
f 46916
 
2.9%
Other values (5) 224196
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1624916
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 361162
22.2%
o 318028
19.6%
a 136340
 
8.4%
e 129570
 
8.0%
s 91470
 
5.6%
m 91470
 
5.6%
n 89424
 
5.5%
d 89424
 
5.5%
T 46916
 
2.9%
f 46916
 
2.9%
Other values (5) 224196
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1624916
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 361162
22.2%
o 318028
19.6%
a 136340
 
8.4%
e 129570
 
8.0%
s 91470
 
5.6%
m 91470
 
5.6%
n 89424
 
5.5%
d 89424
 
5.5%
T 46916
 
2.9%
f 46916
 
2.9%
Other values (5) 224196
13.8%

Fabricante
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
ABB
47250 
Schneider
47161 
GE
44008 
Siemens
41849 

Length

Max length9
Median length7
Mean length5.254166
Min length2

Characters and Unicode

Total characters947158
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSiemens
2nd rowSiemens
3rd rowSiemens
4th rowSiemens
5th rowSiemens

Common Values

ValueCountFrequency (%)
ABB 47250
26.2%
Schneider 47161
26.2%
GE 44008
24.4%
Siemens 41849
23.2%

Length

2025-03-28T00:07:45.405439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T00:07:45.463647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
abb 47250
26.2%
schneider 47161
26.2%
ge 44008
24.4%
siemens 41849
23.2%

Most occurring characters

ValueCountFrequency (%)
e 178020
18.8%
B 94500
10.0%
n 89010
9.4%
S 89010
9.4%
i 89010
9.4%
A 47250
 
5.0%
h 47161
 
5.0%
c 47161
 
5.0%
d 47161
 
5.0%
r 47161
 
5.0%
Other values (4) 171714
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 947158
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 178020
18.8%
B 94500
10.0%
n 89010
9.4%
S 89010
9.4%
i 89010
9.4%
A 47250
 
5.0%
h 47161
 
5.0%
c 47161
 
5.0%
d 47161
 
5.0%
r 47161
 
5.0%
Other values (4) 171714
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 947158
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 178020
18.8%
B 94500
10.0%
n 89010
9.4%
S 89010
9.4%
i 89010
9.4%
A 47250
 
5.0%
h 47161
 
5.0%
c 47161
 
5.0%
d 47161
 
5.0%
r 47161
 
5.0%
Other values (4) 171714
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 947158
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 178020
18.8%
B 94500
10.0%
n 89010
9.4%
S 89010
9.4%
i 89010
9.4%
A 47250
 
5.0%
h 47161
 
5.0%
c 47161
 
5.0%
d 47161
 
5.0%
r 47161
 
5.0%
Other values (4) 171714
18.1%

Modelo
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Y200
51421 
X100
45473 
Z300
44757 
M400
38617 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters721072
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZ300
2nd rowZ300
3rd rowZ300
4th rowZ300
5th rowZ300

Common Values

ValueCountFrequency (%)
Y200 51421
28.5%
X100 45473
25.2%
Z300 44757
24.8%
M400 38617
21.4%

Length

2025-03-28T00:07:45.548980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T00:07:45.595808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
y200 51421
28.5%
x100 45473
25.2%
z300 44757
24.8%
m400 38617
21.4%

Most occurring characters

ValueCountFrequency (%)
0 360536
50.0%
Y 51421
 
7.1%
2 51421
 
7.1%
X 45473
 
6.3%
1 45473
 
6.3%
Z 44757
 
6.2%
3 44757
 
6.2%
M 38617
 
5.4%
4 38617
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 721072
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 360536
50.0%
Y 51421
 
7.1%
2 51421
 
7.1%
X 45473
 
6.3%
1 45473
 
6.3%
Z 44757
 
6.2%
3 44757
 
6.2%
M 38617
 
5.4%
4 38617
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 721072
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 360536
50.0%
Y 51421
 
7.1%
2 51421
 
7.1%
X 45473
 
6.3%
1 45473
 
6.3%
Z 44757
 
6.2%
3 44757
 
6.2%
M 38617
 
5.4%
4 38617
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 721072
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 360536
50.0%
Y 51421
 
7.1%
2 51421
 
7.1%
X 45473
 
6.3%
1 45473
 
6.3%
Z 44757
 
6.2%
3 44757
 
6.2%
M 38617
 
5.4%
4 38617
 
5.4%

Potencia_kW
Real number (ℝ)

Distinct477
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2470.801
Minimum75
Maximum4990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-28T00:07:45.678285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum75
5-th percentile269
Q11250
median2390
Q33762
95-th percentile4806
Maximum4990
Range4915
Interquartile range (IQR)2512

Descriptive statistics

Standard deviation1442.0588
Coefficient of variation (CV)0.58364019
Kurtosis-1.1560843
Mean2470.801
Median Absolute Deviation (MAD)1217
Skewness0.11078435
Sum4.4540636 × 108
Variance2079533.5
MonotonicityNot monotonic
2025-03-28T00:07:45.779150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2157 1082
 
0.6%
194 1007
 
0.6%
3778 976
 
0.5%
1706 904
 
0.5%
4932 865
 
0.5%
3935 815
 
0.5%
100 792
 
0.4%
953 780
 
0.4%
633 729
 
0.4%
4624 710
 
0.4%
Other values (467) 171608
95.2%
ValueCountFrequency (%)
75 528
0.3%
91 396
0.2%
100 792
0.4%
131 567
0.3%
132 162
 
0.1%
154 270
 
0.1%
163 506
0.3%
182 504
0.3%
183 255
 
0.1%
187 169
 
0.1%
ValueCountFrequency (%)
4990 320
 
0.2%
4989 195
 
0.1%
4980 396
0.2%
4978 336
 
0.2%
4966 306
 
0.2%
4948 522
0.3%
4944 540
0.3%
4940 306
 
0.2%
4932 865
0.5%
4929 275
 
0.2%
Distinct488
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5334.0576
Minimum509
Maximum9988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-28T00:07:45.881686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum509
5-th percentile1100
Q13105
median5328
Q37713
95-th percentile9489
Maximum9988
Range9479
Interquartile range (IQR)4608

Descriptive statistics

Standard deviation2729.143
Coefficient of variation (CV)0.51164482
Kurtosis-1.2478442
Mean5334.0576
Median Absolute Deviation (MAD)2319
Skewness-0.047832283
Sum9.615599 × 108
Variance7448221.3
MonotonicityNot monotonic
2025-03-28T00:07:45.979285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4455 1098
 
0.6%
9335 1044
 
0.6%
9660 861
 
0.5%
7529 851
 
0.5%
6355 844
 
0.5%
7358 840
 
0.5%
547 826
 
0.5%
3183 814
 
0.5%
7685 792
 
0.4%
3630 780
 
0.4%
Other values (478) 171518
95.1%
ValueCountFrequency (%)
509 273
 
0.2%
535 441
0.2%
547 826
0.5%
565 240
 
0.1%
587 414
0.2%
601 380
0.2%
665 506
0.3%
684 441
0.2%
692 408
0.2%
695 396
0.2%
ValueCountFrequency (%)
9988 300
0.2%
9958 340
0.2%
9954 619
0.3%
9920 460
0.3%
9887 260
0.1%
9876 550
0.3%
9859 182
 
0.1%
9839 323
0.2%
9819 462
0.3%
9789 209
 
0.1%

ID_Orden
Real number (ℝ)

Distinct10000
Distinct (%)5.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4991.8178
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-28T00:07:46.095978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile498
Q12483
median4986
Q37493
95-th percentile9503
Maximum10000
Range9999
Interquartile range (IQR)5010

Descriptive statistics

Standard deviation2890.227
Coefficient of variation (CV)0.57899289
Kurtosis-1.2013275
Mean4991.8178
Median Absolute Deviation (MAD)2505
Skewness0.0039861842
Sum8.9986002 × 108
Variance8353412.3
MonotonicityNot monotonic
2025-03-28T00:07:46.279963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9150 31
 
< 0.1%
8200 31
 
< 0.1%
8163 31
 
< 0.1%
7700 31
 
< 0.1%
8112 31
 
< 0.1%
7501 31
 
< 0.1%
7319 31
 
< 0.1%
6925 31
 
< 0.1%
6970 31
 
< 0.1%
1974 31
 
< 0.1%
Other values (9990) 179957
99.8%
ValueCountFrequency (%)
1 13
< 0.1%
2 14
< 0.1%
3 24
< 0.1%
4 16
< 0.1%
5 17
< 0.1%
6 21
< 0.1%
7 21
< 0.1%
8 18
< 0.1%
9 12
< 0.1%
10 13
< 0.1%
ValueCountFrequency (%)
10000 21
< 0.1%
9999 14
< 0.1%
9998 19
< 0.1%
9997 20
< 0.1%
9996 27
< 0.1%
9995 21
< 0.1%
9994 15
< 0.1%
9993 20
< 0.1%
9992 22
< 0.1%
9991 17
< 0.1%
Distinct10000
Distinct (%)5.5%
Missing1
Missing (%)< 0.1%
Memory size1.4 MiB
Minimum2020-01-01 00:00:00
Maximum2021-02-20 15:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-28T00:07:46.379736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:46.512882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size1.4 MiB
Preventivo
90837 
Correctivo
89430 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1802670
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPreventivo
2nd rowPreventivo
3rd rowPreventivo
4th rowPreventivo
5th rowPreventivo

Common Values

ValueCountFrequency (%)
Preventivo 90837
50.4%
Correctivo 89430
49.6%
(Missing) 1
 
< 0.1%

Length

2025-03-28T00:07:46.623283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T00:07:46.678658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
preventivo 90837
50.4%
correctivo 89430
49.6%

Most occurring characters

ValueCountFrequency (%)
v 271104
15.0%
e 271104
15.0%
o 269697
15.0%
r 269697
15.0%
i 180267
10.0%
t 180267
10.0%
P 90837
 
5.0%
n 90837
 
5.0%
C 89430
 
5.0%
c 89430
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1802670
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
v 271104
15.0%
e 271104
15.0%
o 269697
15.0%
r 269697
15.0%
i 180267
10.0%
t 180267
10.0%
P 90837
 
5.0%
n 90837
 
5.0%
C 89430
 
5.0%
c 89430
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1802670
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
v 271104
15.0%
e 271104
15.0%
o 269697
15.0%
r 269697
15.0%
i 180267
10.0%
t 180267
10.0%
P 90837
 
5.0%
n 90837
 
5.0%
C 89430
 
5.0%
c 89430
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1802670
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
v 271104
15.0%
e 271104
15.0%
o 269697
15.0%
r 269697
15.0%
i 180267
10.0%
t 180267
10.0%
P 90837
 
5.0%
n 90837
 
5.0%
C 89430
 
5.0%
c 89430
 
5.0%

Costo_Mantenimiento
Real number (ℝ)

Distinct9873
Distinct (%)5.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5047.4041
Minimum101.51
Maximum9998.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-28T00:07:46.746437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum101.51
5-th percentile573.85
Q12558.45
median5054.7241
Q37499.55
95-th percentile9507.75
Maximum9998.08
Range9896.57
Interquartile range (IQR)4941.1

Descriptive statistics

Standard deviation2858.6005
Coefficient of variation (CV)0.56635063
Kurtosis-1.19032
Mean5047.4041
Median Absolute Deviation (MAD)2474.0159
Skewness-0.012417328
Sum9.0988039 × 108
Variance8171596.8
MonotonicityNot monotonic
2025-03-28T00:07:46.862616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5054.724115 536
 
0.3%
5255.835064 470
 
0.3%
5823.96 50
 
< 0.1%
4004.46 48
 
< 0.1%
7910.63 46
 
< 0.1%
2957.17 46
 
< 0.1%
5622.62 45
 
< 0.1%
4719.74 44
 
< 0.1%
7433.6 44
 
< 0.1%
5132.81 44
 
< 0.1%
Other values (9863) 178894
99.2%
ValueCountFrequency (%)
101.51 14
< 0.1%
104.41 19
< 0.1%
104.5 19
< 0.1%
104.85 10
 
< 0.1%
105.54 26
< 0.1%
105.63 22
< 0.1%
105.65 23
< 0.1%
107.38 21
< 0.1%
109.85 14
< 0.1%
109.95 10
 
< 0.1%
ValueCountFrequency (%)
9998.08 18
< 0.1%
9995.14 22
< 0.1%
9994.73 25
< 0.1%
9994.59 18
< 0.1%
9993.47 20
< 0.1%
9992.88 15
< 0.1%
9991.96 14
< 0.1%
9991.82 25
< 0.1%
9991.49 15
< 0.1%
9991.48 18
< 0.1%

Duracion_Horas
Real number (ℝ)

Distinct47
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean23.925133
Minimum1
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-28T00:07:46.944209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q112
median24
Q336
95-th percentile45
Maximum47
Range46
Interquartile range (IQR)24

Descriptive statistics

Standard deviation13.594612
Coefficient of variation (CV)0.56821466
Kurtosis-1.1894061
Mean23.925133
Median Absolute Deviation (MAD)12
Skewness-0.0013999604
Sum4312912
Variance184.81346
MonotonicityNot monotonic
2025-03-28T00:07:47.027781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
23 4282
 
2.4%
5 4259
 
2.4%
3 4203
 
2.3%
20 4184
 
2.3%
30 4153
 
2.3%
29 4118
 
2.3%
25 4106
 
2.3%
2 4083
 
2.3%
4 4068
 
2.3%
47 4047
 
2.2%
Other values (37) 138764
77.0%
ValueCountFrequency (%)
1 3736
2.1%
2 4083
2.3%
3 4203
2.3%
4 4068
2.3%
5 4259
2.4%
6 4038
2.2%
7 3978
2.2%
8 3374
1.9%
9 3494
1.9%
10 3731
2.1%
ValueCountFrequency (%)
47 4047
2.2%
46 4005
2.2%
45 3895
2.2%
44 3723
2.1%
43 3776
2.1%
42 3267
1.8%
41 3847
2.1%
40 3899
2.2%
39 3658
2.0%
38 3704
2.1%

Ubicacion
Categorical

Distinct4
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size1.4 MiB
Planta Este
46145 
Planta Sur
46077 
Planta Oeste
44191 
Planta Norte
43854 

Length

Max length12
Median length11
Mean length11.23281
Min length10

Characters and Unicode

Total characters2024905
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPlanta Oeste
2nd rowPlanta Oeste
3rd rowPlanta Oeste
4th rowPlanta Oeste
5th rowPlanta Oeste

Common Values

ValueCountFrequency (%)
Planta Este 46145
25.6%
Planta Sur 46077
25.6%
Planta Oeste 44191
24.5%
Planta Norte 43854
24.3%
(Missing) 1
 
< 0.1%

Length

2025-03-28T00:07:47.112981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T00:07:47.164473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
planta 180267
50.0%
este 46145
 
12.8%
sur 46077
 
12.8%
oeste 44191
 
12.3%
norte 43854
 
12.2%

Most occurring characters

ValueCountFrequency (%)
a 360534
17.8%
t 314457
15.5%
P 180267
8.9%
l 180267
8.9%
n 180267
8.9%
180267
8.9%
e 178381
8.8%
s 90336
 
4.5%
r 89931
 
4.4%
E 46145
 
2.3%
Other values (5) 224053
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2024905
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 360534
17.8%
t 314457
15.5%
P 180267
8.9%
l 180267
8.9%
n 180267
8.9%
180267
8.9%
e 178381
8.8%
s 90336
 
4.5%
r 89931
 
4.4%
E 46145
 
2.3%
Other values (5) 224053
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2024905
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 360534
17.8%
t 314457
15.5%
P 180267
8.9%
l 180267
8.9%
n 180267
8.9%
180267
8.9%
e 178381
8.8%
s 90336
 
4.5%
r 89931
 
4.4%
E 46145
 
2.3%
Other values (5) 224053
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2024905
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 360534
17.8%
t 314457
15.5%
P 180267
8.9%
l 180267
8.9%
n 180267
8.9%
180267
8.9%
e 178381
8.8%
s 90336
 
4.5%
r 89931
 
4.4%
E 46145
 
2.3%
Other values (5) 224053
11.1%

ID_Registro
Real number (ℝ)

Distinct9000
Distinct (%)5.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4504.5059
Minimum1
Maximum9000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-28T00:07:47.228379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile459
Q12253
median4510
Q36758
95-th percentile8550.7
Maximum9000
Range8999
Interquartile range (IQR)4505

Descriptive statistics

Standard deviation2598.2514
Coefficient of variation (CV)0.57681162
Kurtosis-1.2017778
Mean4504.5059
Median Absolute Deviation (MAD)2252
Skewness-0.00077442096
Sum8.1201377 × 108
Variance6750910.2
MonotonicityNot monotonic
2025-03-28T00:07:47.323267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5275 33
 
< 0.1%
5170 33
 
< 0.1%
4151 33
 
< 0.1%
7054 33
 
< 0.1%
3459 33
 
< 0.1%
3719 33
 
< 0.1%
3427 33
 
< 0.1%
8857 33
 
< 0.1%
2847 33
 
< 0.1%
646 33
 
< 0.1%
Other values (8990) 179937
99.8%
ValueCountFrequency (%)
1 21
< 0.1%
2 20
< 0.1%
3 13
< 0.1%
4 22
< 0.1%
5 29
< 0.1%
6 26
< 0.1%
7 22
< 0.1%
8 15
< 0.1%
9 24
< 0.1%
10 19
< 0.1%
ValueCountFrequency (%)
9000 16
< 0.1%
8999 22
< 0.1%
8998 21
< 0.1%
8997 21
< 0.1%
8996 16
< 0.1%
8995 16
< 0.1%
8994 20
< 0.1%
8993 19
< 0.1%
8992 26
< 0.1%
8991 16
< 0.1%
Distinct9000
Distinct (%)5.0%
Missing1
Missing (%)< 0.1%
Memory size1.4 MiB
Minimum2020-01-01 00:00:00
Maximum2021-01-09 23:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-28T00:07:47.694462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:47.795374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Temperatura_C
Real number (ℝ)

Distinct5909
Distinct (%)3.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean99.593918
Minimum50.06
Maximum149.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-28T00:07:47.877975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50.06
5-th percentile55.09
Q174.42
median99.49
Q3124.83
95-th percentile145.01
Maximum149.99
Range99.93
Interquartile range (IQR)50.41

Descriptive statistics

Standard deviation28.882249
Coefficient of variation (CV)0.29000013
Kurtosis-1.2076461
Mean99.593918
Median Absolute Deviation (MAD)25.18
Skewness0.022328308
Sum17953497
Variance834.18429
MonotonicityNot monotonic
2025-03-28T00:07:47.944193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102.6451011 578
 
0.3%
136.92 132
 
0.1%
70.86 125
 
0.1%
131.6 119
 
0.1%
96.71 113
 
0.1%
83.96 110
 
0.1%
94.76 109
 
0.1%
74.74 107
 
0.1%
96.64 106
 
0.1%
57.43 106
 
0.1%
Other values (5899) 178662
99.1%
ValueCountFrequency (%)
50.06 20
 
< 0.1%
50.09 64
< 0.1%
50.15 42
< 0.1%
50.16 15
 
< 0.1%
50.17 24
 
< 0.1%
50.18 17
 
< 0.1%
50.19 13
 
< 0.1%
50.2 29
< 0.1%
50.21 19
 
< 0.1%
50.22 54
< 0.1%
ValueCountFrequency (%)
149.99 16
< 0.1%
149.98 26
< 0.1%
149.97 16
< 0.1%
149.96 20
< 0.1%
149.95 23
< 0.1%
149.88 38
< 0.1%
149.87 17
< 0.1%
149.8 13
 
< 0.1%
149.78 18
< 0.1%
149.75 35
< 0.1%

Vibracion_mm_s
Real number (ℝ)

Distinct492
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.5685823
Minimum0.1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-28T00:07:48.028230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.34
Q11.32
median2.57
Q33.83
95-th percentile4.76
Maximum5
Range4.9
Interquartile range (IQR)2.51

Descriptive statistics

Standard deviation1.4215532
Coefficient of variation (CV)0.55343883
Kurtosis-1.2161375
Mean2.5685823
Median Absolute Deviation (MAD)1.25
Skewness-0.015550859
Sum463030.62
Variance2.0208134
MonotonicityNot monotonic
2025-03-28T00:07:48.095020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.2 672
 
0.4%
4.77 607
 
0.3%
0.64 590
 
0.3%
3.89 580
 
0.3%
4.09 576
 
0.3%
2.82 555
 
0.3%
0.66 554
 
0.3%
2.13 545
 
0.3%
1.12 543
 
0.3%
1.88 541
 
0.3%
Other values (482) 174504
96.8%
ValueCountFrequency (%)
0.1 215
0.1%
0.11 360
0.2%
0.12 393
0.2%
0.13 453
0.3%
0.14 259
0.1%
0.15 513
0.3%
0.16 399
0.2%
0.17 347
0.2%
0.18 384
0.2%
0.19 460
0.3%
ValueCountFrequency (%)
5 161
 
0.1%
4.99 304
0.2%
4.98 274
0.2%
4.97 312
0.2%
4.96 215
0.1%
4.95 431
0.2%
4.94 497
0.3%
4.93 403
0.2%
4.92 344
0.2%
4.91 369
0.2%

Horas_Operativas
Real number (ℝ)

Distinct8575
Distinct (%)4.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean50069.115
Minimum46
Maximum99991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-03-28T00:07:48.177982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile5089
Q125091
median50064
Q375201
95-th percentile94635
Maximum99991
Range99945
Interquartile range (IQR)50110

Descriptive statistics

Standard deviation28772.455
Coefficient of variation (CV)0.57465475
Kurtosis-1.1985392
Mean50069.115
Median Absolute Deviation (MAD)25063
Skewness-0.01641922
Sum9.0258092 × 109
Variance8.2785415 × 108
MonotonicityNot monotonic
2025-03-28T00:07:48.263395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50058.92667 815
 
0.5%
72401 71
 
< 0.1%
3932 67
 
< 0.1%
54525 65
 
< 0.1%
87945 64
 
< 0.1%
97022 62
 
< 0.1%
35299 62
 
< 0.1%
33614 61
 
< 0.1%
58318 60
 
< 0.1%
62420 58
 
< 0.1%
Other values (8565) 178882
99.2%
ValueCountFrequency (%)
46 20
< 0.1%
96 22
< 0.1%
99 21
< 0.1%
102 17
< 0.1%
131 19
< 0.1%
135 14
< 0.1%
140 24
< 0.1%
142 23
< 0.1%
145 15
< 0.1%
174 15
< 0.1%
ValueCountFrequency (%)
99991 28
< 0.1%
99987 13
< 0.1%
99971 18
< 0.1%
99954 19
< 0.1%
99953 14
< 0.1%
99952 25
< 0.1%
99945 17
< 0.1%
99926 21
< 0.1%
99897 19
< 0.1%
99896 16
< 0.1%

Interactions

2025-03-28T00:07:43.128679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:35.693761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:36.444941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:37.204050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:38.281609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:39.083043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:39.861342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:40.612281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:41.362267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:42.094359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:43.214511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:35.772060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:36.511578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:37.277582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:38.362647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:39.165569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:39.944811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:40.677889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:41.428222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:42.413142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:43.295726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:35.843854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:36.601672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:37.345016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:38.444024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:39.248517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:40.012570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:40.764357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:41.511241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:42.482298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:43.394347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:35.911152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:36.679254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:37.428344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:38.515602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:39.329362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:40.080245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:40.844682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:41.579652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:42.561237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:43.479528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:35.994567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:36.747391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:37.505078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:38.598648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:39.409223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:40.161467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:40.911474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:41.662732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:42.631304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:43.566617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:36.061292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:36.830190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:37.578406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:38.661342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:39.479258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:40.240589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:40.995825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:41.728203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:42.713659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:43.645183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:36.146744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:36.910786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:37.644791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:38.744284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:39.561316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:40.311389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:41.061769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:41.811592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:42.807985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:43.745012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:36.213450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:36.982533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:38.047207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:38.826216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:39.627742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:40.380539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:41.127980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:41.877880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:42.878880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:43.846518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:36.301996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:37.047507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:38.113623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:38.894994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:39.710530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:40.464540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:41.214051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:41.944513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:42.961833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:43.929505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:36.376313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:37.127859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:38.206663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:38.977914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:39.777935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:40.529913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:41.284228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:42.027643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-28T00:07:43.044667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-28T00:07:48.328132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Costo_MantenimientoDuracion_HorasFabricanteHoras_OperativasHoras_Recomendadas_RevisionID_EquipoID_OrdenID_RegistroModeloPotencia_kWTemperatura_CTipo_EquipoTipo_MantenimientoUbicacionVibracion_mm_s
Costo_Mantenimiento1.000-0.0060.032-0.0010.001-0.0290.003-0.0000.040-0.001-0.0000.0270.0090.026-0.000
Duracion_Horas-0.0061.0000.023-0.000-0.0130.0150.0170.0020.029-0.0040.0010.0260.0300.0270.004
Fabricante0.0320.0231.0000.0210.1170.1500.0260.0270.0590.1280.0280.0820.0170.0230.039
Horas_Operativas-0.001-0.0000.0211.0000.016-0.0100.004-0.0030.038-0.0070.0050.0260.0040.000-0.002
Horas_Recomendadas_Revision0.001-0.0130.1170.0161.000-0.018-0.017-0.0090.127-0.0560.0080.1320.0230.0300.001
ID_Equipo-0.0290.0150.150-0.010-0.0181.0000.0140.0150.1440.0090.0050.1450.0410.037-0.014
ID_Orden0.0030.0170.0260.004-0.0170.0141.000-0.0030.0300.002-0.0010.0290.0230.0280.001
ID_Registro-0.0000.0020.027-0.003-0.0090.015-0.0031.0000.032-0.014-0.0060.0310.0000.000-0.006
Modelo0.0400.0290.0590.0380.1270.1440.0300.0321.0000.1520.0330.0620.0170.0190.027
Potencia_kW-0.001-0.0040.128-0.007-0.0560.0090.002-0.0140.1521.0000.0000.1270.0430.025-0.001
Temperatura_C-0.0000.0010.0280.0050.0080.005-0.001-0.0060.0330.0001.0000.0240.0000.000-0.005
Tipo_Equipo0.0270.0260.0820.0260.1320.1450.0290.0310.0620.1270.0241.0000.0110.0200.032
Tipo_Mantenimiento0.0090.0300.0170.0040.0230.0410.0230.0000.0170.0430.0000.0111.0000.0060.000
Ubicacion0.0260.0270.0230.0000.0300.0370.0280.0000.0190.0250.0000.0200.0061.0000.002
Vibracion_mm_s-0.0000.0040.039-0.0020.001-0.0140.001-0.0060.027-0.001-0.0050.0320.0000.0021.000

Missing values

2025-03-28T00:07:44.088044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-28T00:07:44.347071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-28T00:07:44.744850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ID_EquipoTipo_EquipoFabricanteModeloPotencia_kWHoras_Recomendadas_RevisionID_OrdenFecha_OrdenesTipo_MantenimientoCosto_MantenimientoDuracion_HorasUbicacionID_RegistroFecha_RegistrosTemperatura_CVibracion_mm_sHoras_Operativas
01CompresorSiemensZ3003429.07725799.02020-02-03 06:00:00Preventivo404.7237.0Planta Oeste28.02020-01-02 03:00:0093.800.7080054.0
11CompresorSiemensZ3003429.07725799.02020-02-03 06:00:00Preventivo404.7237.0Planta Oeste482.02020-01-21 01:00:00102.992.3174797.0
21CompresorSiemensZ3003429.07725799.02020-02-03 06:00:00Preventivo404.7237.0Planta Oeste1479.02020-03-02 14:00:00145.971.5795240.0
31CompresorSiemensZ3003429.07725799.02020-02-03 06:00:00Preventivo404.7237.0Planta Oeste1805.02020-03-16 04:00:00147.182.496872.0
41CompresorSiemensZ3003429.07725799.02020-02-03 06:00:00Preventivo404.7237.0Planta Oeste2591.02020-04-17 22:00:0058.933.5428186.0
51CompresorSiemensZ3003429.07725799.02020-02-03 06:00:00Preventivo404.7237.0Planta Oeste2650.02020-04-20 09:00:0057.433.0238658.0
61CompresorSiemensZ3003429.07725799.02020-02-03 06:00:00Preventivo404.7237.0Planta Oeste2743.02020-04-24 06:00:0062.160.3219985.0
71CompresorSiemensZ3003429.07725799.02020-02-03 06:00:00Preventivo404.7237.0Planta Oeste3716.02020-06-03 19:00:00106.121.4059768.0
81CompresorSiemensZ3003429.07725799.02020-02-03 06:00:00Preventivo404.7237.0Planta Oeste4650.02020-07-12 17:00:00103.160.5124317.0
91CompresorSiemensZ3003429.07725799.02020-02-03 06:00:00Preventivo404.7237.0Planta Oeste4929.02020-07-24 08:00:0070.104.4197100.0
ID_EquipoTipo_EquipoFabricanteModeloPotencia_kWHoras_Recomendadas_RevisionID_OrdenFecha_OrdenesTipo_MantenimientoCosto_MantenimientoDuracion_HorasUbicacionID_RegistroFecha_RegistrosTemperatura_CVibracion_mm_sHoras_Operativas
180258499MotorSiemensY2003155.09328598.02020-12-24 05:00:00Preventivo4446.6418.0Planta Sur5750.02020-08-27 13:00:0086.021.1822695.0
180259499MotorSiemensY2003155.09328598.02020-12-24 05:00:00Preventivo4446.6418.0Planta Sur6095.02020-09-10 22:00:0062.672.4177284.0
180260499MotorSiemensY2003155.09328598.02020-12-24 05:00:00Preventivo4446.6418.0Planta Sur6151.02020-09-13 06:00:0086.832.1078950.0
180261499MotorSiemensY2003155.09328598.02020-12-24 05:00:00Preventivo4446.6418.0Planta Sur6159.02020-09-13 14:00:0054.580.809149.0
180262499MotorSiemensY2003155.09328598.02020-12-24 05:00:00Preventivo4446.6418.0Planta Sur7180.02020-10-26 03:00:0062.182.6041317.0
180263499MotorSiemensY2003155.09328598.02020-12-24 05:00:00Preventivo4446.6418.0Planta Sur7445.02020-11-06 04:00:0065.470.6111466.0
180264499MotorSiemensY2003155.09328598.02020-12-24 05:00:00Preventivo4446.6418.0Planta Sur7613.02020-11-13 04:00:0088.840.8858159.0
180265499MotorSiemensY2003155.09328598.02020-12-24 05:00:00Preventivo4446.6418.0Planta Sur7631.02020-11-13 22:00:00146.080.7046817.0
180266499MotorSiemensY2003155.09328598.02020-12-24 05:00:00Preventivo4446.6418.0Planta Sur7902.02020-11-25 05:00:00100.304.0357567.0
180267500TransformadorGEM4002001.02031NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN